Since Covid time, AI-adoption has skyrocketed. According to a report by PwC, there was an 18 percent jump in AI adoption within participating companies from 2020 to 2021, with 54 percent headed in that direction. And there’s no better application for AI than for analyzing large quantities of data, like research reports, company financial models, and trading strategies.

Ephod Technology was created to streamline the decision-making process in money management for global money managers with the help of quantitative analysis. “If portfolio managers want to beat that market, they are often stuck analyzing tons of market data. Hiring highly-qualified data scientists can be difficult for smaller, semi-professional institutional investors,” said Headline Principal Brian Yen, who lead the investment. “Ephod’s solutions uncover significant value by enabling those with limited access to machine learning. We believe Ephod will be the best data analytics partner for institutional, or potentially individual investors, who want a competitive edge.” Headline invested in Ephod in August 2021.

Founder Peter Zhou is an amateur woodworking hobbyist, a former engineer at Asia’s leading audio streaming media company KKBOX, and according to his LinkedIn bio, “dreamer and entrepreneur.” We spoke to him about his experience founding EphodTech and what he looks for in a team.

What’s the biggest risk you took when you founded your company? What was the moment when you knew this was the startup idea to build?

I had no clue what to build when I founded the company. The only sure thing was the vision: “We help make better decisions.” We wanted to make AI-related products to help money managers. The biggest risks were to ask my co-founders to work full-time and to ask my advisers to introduce friends to me without clearly knowing what to do.

I interviewed 20 plus fund managers in Hong Kong, Mainland China, and Taiwan with the same questions, until I could precisely predict their pain points within the first five minutes of the conversation. Even though we were not so sure what kind of solutions to build at that time, I just knew our team could create value for them. I realized that fund managers need data analysis (AI) to prioritize their fundamental research, and they need data to generate new ideas. Our startup ideas were always generated around our customers’ pain points and our vision statement. It worked well.

What were the three biggest challenges your company had to overcome?

The biggest challenge is to attract top talent to catch up with our increasingly growing speed. We expect to double our team size every year. Quantitative finance is not new. However, with the advancement of cloud computing and machine learning technologies, we need to attract talent who embrace modern technologies [needed] to build great products.

Data quality is also a challenge. Recently generated data can be more detailed than five years ago. Data can be periodically altered and corrected without notice. Data could be different among different vendors even though it is claimed to be the same. The best solution would be to keep multiple data sources for cross-reference purposes and to adjust our automation according to the nature of the data. Our infrastructure is designed to respond to small changes quickly since it is a constantly improving process.

Our customers’ views of the market are diversified. When we translate qualitative views into quantitative languages, we need to make sure our implementation can be automated and are scale-able.

What’s the proudest moment on your entrepreneurial journey?

I was so inspired by Derek Siver’s TED Talk “How to Start a Movement.” Founding a company is just like starting a movement. In the journey, I was the first one to start a crazy dance. My co-founders were the second and third ones to follow. Then we attracted our advisors from the hedge fund industries to help us; and we attracted customers, investors, and employees; and we attracted more and more. I feel so proud whenever someone joins in on this exceptional journey.

What does success look like for the company?

There is no definition of success in my dictionary. To start up a company is like an enjoyable journey to me. I enjoy creating values and learning new knowledge along the way. Whenever the company is generating value, it is successful. I hope our customers and investors think the same way.

I certainly have some vague expectations on the business scale of the company. It must globalize on day one. Our customers, employees, and investors should come from multiple countries and regions (i.e., Singapore, Hong Kong, Mainland China, and Taiwan). We think big from day one.

What do you do to motivate employees at your company?

The startup idea itself motivates our employees. What we are doing is unique and we are all learning from data from different perspectives every day.

As an early startup, we give stocks to our employees. However, the younger generation seems like they are more motivated by the “why” aspect rather than the monetary factor.

What do you value in being a team member at Ephod?

I value diversity and teamwork. We have people from different majors: Maths, Electrical Engineering, Computer Science, Finance, Quantitative Finance. We worked in traditional financial and internet industries. We might have so many different views on the same problem. Our team work brings innovative solutions to real-world problems.

What’s a mistake you made along the way and how would you go back and fix it?

I don’t see “making mistakes” as a problem, rather [as] a learning curve. We were not able to predict many things, from market demand to product complexity. Failing fast, learning along the way but in the end, we’ll be fine.

Ten months ago, I realized our biggest challenge was to define a target user group and to find their pain points. We then started the pain-point interview for the following two months.

Three months ago, we had our first MVP customer and planned to build our scaleable product. However, our product’s North Star metrics were not clearly defined. We spent a month developing product stories without aiming for clear future business value. We then switched back to customer demand-driven development to focus on the product’s market fit.

All those mistakes were not fatal. However, I think we should [have] resolved it earlier and faster.

What sets your product and service apart?

All our features are customer demand-driven. Most financial data vendors and data analysis companies are competing for a maximum return. They try to tell people, “My data, analysis, and views are correct.” However, we use a different approach. We tell our customers, “Here’s a great data tool. Try to discover the insight yourself.”

We use trading signals as our analysis results instead of data visualizations or financial projection sheets. We believe using trading signals can help our customers make better decisions in a backtested and quantitatively reviewable way.

What are you working on right now and what’s next?

We are working on a scaleable product to fulfill our customers’ needs. We are still focusing on analyzing traditional datasets like historical financial statements, price, and volume. We will soon start using more alternative datasets.

Our first step is to help equity funds. The future possibilities are unlimited to us: bond funds, crypto funds, family offices, corporate finance departments, etc. We are all excited about our future.

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